Multicategory Support Vector Machines , Theory , and Application to the Classification of Microarray Data and Satellite Radiance Data

By Yoonkyung Lee, Cheol-Koo Lee. 2004

Subfields: Computer Vision and Pattern Recognition, Model Architectures

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Citations in the corpus

On Structured Prediction Theory with Calibrated Convex Surrogate Losses (2017, background): “[26] (the score vectors are constrained to sum to zero) that can be built for any task loss of interest.”
Reinforcement learning design for cancer clinical trials (2009, unknown): “Clinical trials of mixed chemoimmunotherapy are developed for metastatic melanoma treatment. For instance, a series of sequential Phase II trials were conducted at M.D. Anderson Cancer Center (Buzaid 2000; Buzaid and Atkins 2001). These trials were based on integrating of IL-2 and interferon-alpha (”
Ranking with Large Margin Principle: Two Approaches (2002, unknown): “This favorably compares to the required by the recent SVM approach to ordinal regression introduced in [7] or the required by the general multi-class approach to SVM [4, 8 ].”
Using distance covariance for improved variable selection with application to learning genetic risk models (2014, unknown): “These data are known as an easy-classified example and have been studied by many [7].”
Doubly Robust Learning for Estimating Individualized Treatment with Censored Data (2014, unknown): “One approach for extending the proposed framework to handle this case is to incorporate techniques developed in multicategory classification (Lee et al., 2004; Liu & Yuan, 2011).”
Multicategory Support Vector Machines, Theory, and Application to the Classiﬁcation of Microarray Data and Satellite Radiance Data (2002, background): “See also Lee (2002).”
Cost-sensitive Multiclass Classification Risk Bounds (2013, methodology): “In this section, we demonstrate that the " Simplex Coding loss " is a special type of the loss of Lee et al. (2004) with a particular rotation of the set C.”
Residual Weighted Learning for Estimating Individualized Treatment Rules (2015, methodology): “Multicategory SVMs (Lee et al. 2004) and penalized logistic regression (Zhu and Hastie 2004) are two examples.”
A Comprehensive Survey of Loss Functions in Machine Learning (2020, unknown): “It should be noted that binary classification can be extended to multi-classification according to one-vs-rest, one-vs-one [26, 27] and so on [28–32].”
Support Vector Machines with Applications (2006, methodology): “[76] VAPNIK, V. (1995). The Nature of Statistical Learning The-”
MULTI-CATEGORY SUPPORT VECTOR MACHINES, FEATURE SELECTION AND SOLUTION PATH (2006, methodology): “According to Wang and Shen (2005), L1MSVM solves the problem: where P =1 w c 1 = P =1 P =1 j w c;j j is an L 1 -norm penalty, s is a tuning parameter, and L ( f; z i ) is the generalized hinge loss 2) can be straightfor-wardly extended to other existing formulations of MSVMs, using di(cid:11)erent f”
Estimating Individualized Treatment Rules Using Outcome Weighted Learning (2012, background): “The case of multicategory SVM has been studied recently (Lee, Lin, and Wahba 2004; Wang and Shen 2006), and a similar generalization may be possible for ﬁnding ITRs involving three or more treatments.”
A Unified View on Multi-class Support Vector Classification (2016, methodology): “was the first multi-class large margin loss known to be Fisher consistent (Lee et al., 2004; Tewari and Bartlett, 2007; Liu, 2007).”